---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: chess-guesser-distilbert
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# chess-guesser-distilbert

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6500

## Model description

More information needed

## Intended uses & limitations

Can be used for elo guessing. 

## Training and evaluation data

Updated this model to include 50,000 games. 25,000 games for Bucket 1(400-1000) and 25,000 games for Bucket 2(1001-1200). 90% training and 10% validation array.
Previously had 2,000 games each. 

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6799        | 1.0   | 1407 | 0.6864          |
| 0.6598        | 2.0   | 2814 | 0.6590          |
| 0.6445        | 3.0   | 4221 | 0.6500          |


### Framework versions

- Transformers 5.9.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2